1. Field of the Invention
The present invention generally relates to electrical, condition responsive systems and methods. More particularly, this invention relates to a method and apparatus for detecting and identifying developing smoke clouds in a monitored area using a sequence of digitized images.
2. Description of the Related Art
Smoke detectors are very important safety devices that can provide an early warning of fire in a monitored area. Considerable efforts have been devoted to improving upon the technology used in smoke detectors as a means of increasing their usefulness and reliability.
One of the and most commonly used methodologies for smoke detectors involves measuring the presence of aerosol particles at the location of a smoke detector's sensor. Such measurements are based either on light scattering phenomena or on the effects due to smoke particle interactions with an ionization current created within the detector. See Rattman, et al., U.S. Pat. No. 5,719,557.
A disadvantage of this approach is that its measurements are limited in terms of their sensing area since such detectors monitor for the presence of smoke only at those points that are in close proximity to the location of the detector's sensor. The successful detection of smoke in a monitored area using this technique greatly depends upon the rate of movement of smoke particles toward the detector's sensor which, depending upon the size of the monitored area, can be located at considerable distance from the initial source of any smoke.
To address this insufficient sample size problem, it has been suggested that air samples be collected at multiple locations in the monitored area and then to guide these samples to the location of the detector's sensor. See Knox, et al., U.S. Pat. No. 6,285,291. Although effectively increasing the extent of spatial sampling within a monitored area, this method has the disadvantage of requiring the installation of multiple sampling tubes at assorted locations throughout the monitored area.
Another approach for smoke detection has been to monitor the light scattering effect of smoke particles on a laser beam that is directed across a monitored area. Rather than sensing smoke in the relatively small vicinity of a single sensor, the laser beam approach effectively senses for smoke along a line that can be extended for a considerable distance throughout the monitored area. See Moore, et al., U.S. Pat. No. 3,973,852. However, a disadvantage of using such a laser beam approach is that, although it may effectively measure smoke conditions at more points within a monitored area that just those points in the vicinity of a single sensor, it still does not provided feedback on the smoke conditions at all or most of the points within the monitored area.
In recent years, multiple practitioners have introduced yet another approach for detecting smoke. Instead of directly measuring the attenuation or scattering of light at the given point in space, their approach uses a digital image that is produced by a TV and identifies the presence of smoke in the viewed area by the effects of the smoke on the video image.
In Grech-Cini's U.S. Pat. No. 6,844,818, each pixel of the image is constantly analyzed statistically against the average parameters across the image. The characteristics that are monitored include brightness, color intensity and spatial contrast. Based on the statistical averages, a Bayesian estimator is produced for each pixel and is compared to values from the current image. Once a significant deviation from the estimate occurs over a range of pixels, a determination is made on whether the anomaly is caused by smoke. The disadvantage of this approach is that it has been found to yield a high rate of false alarms that may be caused by changes in the viewed area's lighting conditions or the occurrence of moving objects in the area.
In Rizzotti at al.'s U.S. Pat. No. 6,937,743, a sequence of images are analyzed to identify changes in the contrast of the image by measuring the attenuation of spatial high frequencies using FFT or FHT. Meanwhile, other practitioners (e.g., see Toreyin et al., MUSCLE, Malaga, 4-5 Nov. 2004) have used a wavelet transform for detecting the of loss in clarity occurring across a sequence of images.
However, it would appear that this entire approach of identifying a image's deterioration of contrast, loss of edges or clarity has fundamental drawbacks. First, its use requires that the content of the image have high contrast with enough details. Second, it is based on an assumption that smoke will be semi-transparent and will gradually attenuate the fine details of the image. In reality, this assumption is found to be valid in only limited cases. For example, it has been observed that thick smoke, at good illumination conditions and with plain background, can actually increase the high frequency content of an image.
The present inventor has disclosed yet another alternative approach in U.S. Publication No. 2005/0100193, where the light sources within the image are analyzed to detect the smoke-induced effects on diffusion and scattering in the vicinity of these sources. Practical as a supplementary technique, the disadvantage of this approach is its requirement for a permanent light source within the video image and its ability to detect smoke only when smoke particles are in the vicinity of a light source.
Similarly, Rattman's U.S. Pat. No. 4,614,968 suggests using strategically placed multi-contrast markers for an imaging system that provides close-up views of these objects with high contrast and edge contents. However, this approach limits the area of detection to direct lines of sight with the viewed markers.
Despite the considerable prior art relating to smoke detectors, there is still a need for smoke detector methods and systems that can more effectively measure smoke conditions throughout the entire volume of a desired monitored area.